The influence of agile HRMS on the organizational performance: The case of Dubai government
Bibliographic record
Abstract
In today's rapidly changing and interconnected world, governments are aware that they need to adapt their operations to effectively navigate the complexities they face. To address these challenges and operate more efficiently, governments must forge partnerships, embrace innovation, demonstrate effective leadership, and, most importantly, cultivate a skilled workforce. Recognizing this need, this study focuses on examining the impact of Agile HRMS on organizational performance within the Dubai government. To investigate this relationship, a survey was conducted involving 107 employees from various government departments in Dubai. The results of the survey revealed significant and positive correlations between all the dimensions of Agile HRMS (namely, Agile talent acquisition, Agile employee engagement, and Agile learning and development) and organizational performance. These findings underscore the significance of adopting Agile HRMS approaches in enhancing the professional development and effectiveness of government operations. Therefore, it is recommended that policymakers within the government sector adopt and integrate Agile HRMS practices, as doing so can create an environment conducive to increased productivity and success across these organizations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".